{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hudson and Thames Skill Set Challenge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Author: [@mirca](https://www.github.com/mirca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we are going to see how to programmatically access stocks price data and\n",
    "construct a minimum spanning tree (MST) graph based on those data. The MST graph will reveal\n",
    "stocks dependencies which can be further leveraged into tasks such as hierachical risk parity portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas_datareader as pdr\n",
    "import requests\n",
    "import bs4 as bs\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from core import compute_adjacency_mst_and_distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing financial data from Yahoo! finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step we are going to download stocks prices data from Yahoo! finance.\n",
    "For that, I chose to pick the 50 most representative stocks of the S&P 500 index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following lines of code web scrapes the website https://www.slickcharts.com/sp500 and gets the 50 most representative stocks\n",
    "of the S&P 500 index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = requests.get('https://www.slickcharts.com/sp500')\n",
    "soup = bs.BeautifulSoup(resp.text, 'html.parser')\n",
    "table = soup.find('table', {'class': 'table table-hover table-borderless table-sm'})\n",
    "stocks = [fn['href'][len(\"/symbol/\"):] for fn in table.find_all('a') if fn['href'].startswith('/symbol')]\n",
    "stocks_ = [stocks[i] for i in range(len(stocks)) if i % 2 == 0][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MSFT', 'AAPL', 'AMZN', 'FB', 'BRK.B', 'GOOG', 'GOOGL', 'JNJ', 'JPM', 'V', 'PG', 'T', 'UNH', 'MA', 'HD', 'INTC', 'VZ', 'KO', 'BAC', 'XOM', 'MRK', 'DIS', 'PFE', 'PEP', 'CMCSA', 'CVX', 'ADBE', 'CSCO', 'NVDA', 'WMT', 'NFLX', 'CRM', 'WFC', 'MCD', 'ABT', 'BMY', 'COST', 'BA', 'C', 'PM', 'NEE', 'MDT', 'ABBV', 'PYPL', 'AMGN', 'TMO', 'LLY', 'HON', 'ACN', 'IBM']\n"
     ]
    }
   ],
   "source": [
    "print(stocks_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the ticker names, let's download the price data from Yahoo! finance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mirca/opt/miniconda3/lib/python3.7/site-packages/pandas_datareader/base.py:270: SymbolWarning: Failed to read symbol: 'BRK.B', replacing with NaN.\n",
      "  warnings.warn(msg.format(sym), SymbolWarning)\n"
     ]
    }
   ],
   "source": [
    "prices = pdr.get_data_yahoo(stocks_, start = \"2018-12-31\", end = \"2019-12-31\")[['Adj Close']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop any stocks that have missing values and let's also clean up the attributes of our data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.dropna(axis='columns', inplace=True)\n",
    "prices.columns = prices.columns.droplevel(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the log returns of the stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_returns = prices.apply(np.log).apply(np.diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>JPM</th>\n",
       "      <th>V</th>\n",
       "      <th>PG</th>\n",
       "      <th>...</th>\n",
       "      <th>NEE</th>\n",
       "      <th>MDT</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>PYPL</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>TMO</th>\n",
       "      <th>LLY</th>\n",
       "      <th>HON</th>\n",
       "      <th>ACN</th>\n",
       "      <th>IBM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.004440</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.024440</td>\n",
       "      <td>0.034415</td>\n",
       "      <td>0.009839</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>-0.010125</td>\n",
       "      <td>0.017164</td>\n",
       "      <td>0.007400</td>\n",
       "      <td>-0.006987</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023222</td>\n",
       "      <td>-0.033993</td>\n",
       "      <td>-0.032635</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>-0.014332</td>\n",
       "      <td>-0.019720</td>\n",
       "      <td>-0.007285</td>\n",
       "      <td>-0.002197</td>\n",
       "      <td>-0.002983</td>\n",
       "      <td>0.013457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.037482</td>\n",
       "      <td>-0.104924</td>\n",
       "      <td>-0.025566</td>\n",
       "      <td>-0.029469</td>\n",
       "      <td>-0.028898</td>\n",
       "      <td>-0.028086</td>\n",
       "      <td>-0.016018</td>\n",
       "      <td>-0.014314</td>\n",
       "      <td>-0.036702</td>\n",
       "      <td>-0.007036</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002476</td>\n",
       "      <td>-0.028496</td>\n",
       "      <td>-0.033504</td>\n",
       "      <td>-0.043620</td>\n",
       "      <td>-0.015333</td>\n",
       "      <td>-0.033600</td>\n",
       "      <td>-0.031569</td>\n",
       "      <td>-0.013441</td>\n",
       "      <td>-0.034738</td>\n",
       "      <td>-0.020165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.045460</td>\n",
       "      <td>0.041803</td>\n",
       "      <td>0.048851</td>\n",
       "      <td>0.046061</td>\n",
       "      <td>0.052390</td>\n",
       "      <td>0.050021</td>\n",
       "      <td>0.016644</td>\n",
       "      <td>0.036202</td>\n",
       "      <td>0.042179</td>\n",
       "      <td>0.020205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018249</td>\n",
       "      <td>0.030882</td>\n",
       "      <td>0.031709</td>\n",
       "      <td>0.049666</td>\n",
       "      <td>0.033612</td>\n",
       "      <td>0.044208</td>\n",
       "      <td>0.029652</td>\n",
       "      <td>0.034383</td>\n",
       "      <td>0.038147</td>\n",
       "      <td>0.038314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001275</td>\n",
       "      <td>-0.002228</td>\n",
       "      <td>0.033776</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>-0.001996</td>\n",
       "      <td>-0.006435</td>\n",
       "      <td>0.000695</td>\n",
       "      <td>0.017871</td>\n",
       "      <td>-0.004008</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>-0.066621</td>\n",
       "      <td>0.014490</td>\n",
       "      <td>0.007621</td>\n",
       "      <td>0.013367</td>\n",
       "      <td>0.013393</td>\n",
       "      <td>0.005393</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>0.007050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007225</td>\n",
       "      <td>0.018884</td>\n",
       "      <td>0.016476</td>\n",
       "      <td>0.031937</td>\n",
       "      <td>0.007358</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>0.022961</td>\n",
       "      <td>-0.001887</td>\n",
       "      <td>0.005424</td>\n",
       "      <td>0.003684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007287</td>\n",
       "      <td>0.007010</td>\n",
       "      <td>0.004637</td>\n",
       "      <td>0.020157</td>\n",
       "      <td>0.012742</td>\n",
       "      <td>0.023004</td>\n",
       "      <td>0.009153</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>0.024975</td>\n",
       "      <td>0.014119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols      MSFT      AAPL      AMZN        FB      GOOG     GOOGL       JNJ  \\\n",
       "0       -0.004440  0.001140  0.024440  0.034415  0.009839  0.009259 -0.010125   \n",
       "1       -0.037482 -0.104924 -0.025566 -0.029469 -0.028898 -0.028086 -0.016018   \n",
       "2        0.045460  0.041803  0.048851  0.046061  0.052390  0.050021  0.016644   \n",
       "3        0.001275 -0.002228  0.033776  0.000725 -0.002169 -0.001996 -0.006435   \n",
       "4        0.007225  0.018884  0.016476  0.031937  0.007358  0.008745  0.022961   \n",
       "\n",
       "Symbols       JPM         V        PG  ...       NEE       MDT      ABBV  \\\n",
       "0        0.017164  0.007400 -0.006987  ... -0.023222 -0.033993 -0.032635   \n",
       "1       -0.014314 -0.036702 -0.007036  ... -0.002476 -0.028496 -0.033504   \n",
       "2        0.036202  0.042179  0.020205  ...  0.018249  0.030882  0.031709   \n",
       "3        0.000695  0.017871 -0.004008  ... -0.001450 -0.066621  0.014490   \n",
       "4       -0.001887  0.005424  0.003684  ...  0.007287  0.007010  0.004637   \n",
       "\n",
       "Symbols      PYPL      AMGN       TMO       LLY       HON       ACN       IBM  \n",
       "0        0.019548 -0.014332 -0.019720 -0.007285 -0.002197 -0.002983  0.013457  \n",
       "1       -0.043620 -0.015333 -0.033600 -0.031569 -0.013441 -0.034738 -0.020165  \n",
       "2        0.049666  0.033612  0.044208  0.029652  0.034383  0.038147  0.038314  \n",
       "3        0.007621  0.013367  0.013393  0.005393  0.005556  0.003467  0.007050  \n",
       "4        0.020157  0.012742  0.023004  0.009153 -0.002071  0.024975  0.014119  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next line we are going to compute the [adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix) of a minimum spanning tree graph\n",
    "and the corresponding distances between each pair of stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances, adjacency = compute_adjacency_mst_and_distances(log_returns)\n",
    "adjacency_df = pd.DataFrame(adjacency, columns=prices.columns, index=prices.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations of the MST (adjacency matrix and graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the adjacency matrix of our tree graph and the graph itself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "g = sns.heatmap(adjacency_df, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The adjacency matrix tell us which stocks are conditionally dependent in the graph. In the plot\n",
    "above, the white squares represent pairs of stocks that are dependent and therefore have an edge between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot the network itself so that we can recognize its tree structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = {v: k for v, k in enumerate(list(adjacency_df.columns))}\n",
    "plt.figure(figsize=(10, 10))\n",
    "G = nx.from_numpy_matrix(adjacency)\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw_networkx_nodes(G, pos, node_size=300, alpha=.3)\n",
    "nx.draw_networkx_edges(G, pos, width=1.0)\n",
    "_ = nx.draw_networkx_labels(G, pos, labels)\n",
    "_ = plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above is easier to see some clusters structures, e.g., formed by the tech companies Facebook (FB), Netflix (NFLX), Amazon (AMZN), and Microsoft (MSFT), or pharmaceutical corporations Pfizer (PFE), Eli Lilly and Company (LLY), UnitedHealth Group (UNH), and Merck & Co (MRK)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also draw a dendrogram from the distance matrix: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = linkage(np.triu(distances, k=1))\n",
    "plt.figure(figsize=(15, 5))\n",
    "dendrogram(clusters, labels=adjacency_df.columns)\n",
    "plt.xlabel('Tickers', fontsize=12)\n",
    "plt.ylabel('Cluster Leaves Distances', fontsize=12)\n",
    "plt.title('Hierarchical Clustering Dendrogram', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we saw how to construct a minimum spanning tree graph from a set of stocks prices. The MST\n",
    "may be used to reveal the hierarchical structure of a set stocks via their conditional dependency, which is\n",
    "directly encoded on the edges of the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
